Bespoke kernels
A bridge trained on your distribution.
We run our frontier diffusion model over your features and return a lightweight mixture model -- weights and all. All run on our distributed GPU system.
What ships back
01
A fitted GMM bridge
Drift weights for a Gaussian-mixture Schrödinger bridge fit to your target samples — not a generic checkpoint with your data bolted on.
02
A CUDA kernel
Compiled, benchmarked, and deliberately small. Sub-millisecond evaluation on a single device, with no framework runtime required at inference time.
03
The training run
Shard logs, convergence traces, and a holdout evaluation — so you can see how the fit behaved rather than taking the weights on faith.
Your infrastructure stays empty
Training runs on our bridge cluster — parallel shards across GPU workers, aggregated into one set of drift weights. You never provision a training box, and you never wait behind your own queue.
Small enough to embed
A mixture bridge is a fraction of the size of a diffusion model at comparable sample quality on structured data. That's the whole point: it fits where a general-purpose generator won't.
Plans
Free to configure. Pay when it trains.
Sign up and you can stage a dataset, set up a fit and read the SDK without paying anything. The Developer plan is what puts batch jobs on our compute — bridge training, flow matching and hosted inference against the kernels that come back.
A fit is one kernel trained against one dataset. Volume, annual terms and training inside your own VPC are scoped as an engagement.
Send us your samples.
Upload a target distribution and we'll tell you what the fit will look like before you commit to it.
Upload training data